Data Visualization is the process of transforming Data, information, and knowledge into visual form, making use of humans’ natural visual capabilities which reveals relationships in Data sets that are not evident from the raw Data, by using mathematical techniques to reduce the number of dimensions in the Data set while preserving the relevant inherent properties. In this paper, we formulated Data Visualization as a Quadric Assignment Problem (QAP), and then presented an Artificial Bee Colony (ABC) to solve the resulted discrete optimization problem. The idea behind this approach is to provide mechanisms based on ABC to overcome trapped in local minima and improving the resulted solutions. To demonstrate the application of ABC on discrete optimization in Data Visualization, we used a Database of electricity load and compared the results to other popular methods such as SOM, MDS and Sammon’s map. The results show that QAP-ABC has high performance with compared others.

Nowadays, companies have been moving their IT infrastructure from own Data centers to specialized public cloud providers. While there are cost benefits, the security issue is one of the major concerns in cloud computing due to the number of companies that use cloud storage to save their personal Data keep increasing. Many honeypots have been used in the past, but they were difficult to use due to a lack of Data Visualization and attack analysis. To learn more about attackers, their motivations and techniques, honeypots are used to investigate how attackers attempt to hack an information system and provide useful insight into potential security flaws. This honeypot allows to monitor attacks by pretending to be actual machines with valuable and sensitive Data, such that attackers interact with them. For this research, honeypot was set up on DigitalOcean cloud and the experimental method performs and result of the implementation in this research use real attack since the honeypot deployed on the cloud and exposed to the Internet. The results show that Cowrie honeypot able to collect Data that is valuable to security researcher or network administrator for future research to make analysis. It is believed by implementing Cowrie honeypot using ELK stack on cloud platform will assist on detection and prevention for SSH attacks.

The proposed software solution is a tool developed for the analysis, forecast and Visualization of geophysical Data, which is collected and provided by a set of spatially distributed heterogeneous Data repositories via standard web protocols (HTTP, HTTPs, FTP, etc. ). They include ground magnetic observatories and stations, satellites, as well as various numerical models based on geophysical standards and specifications. The technological stack is limited with the tool’s web-based implementation and represented by integrated client-and server-side technologies with specialized frameworks and APIs. Client-side implementation is represented by several markup, styling and interaction software technologies, which are HTML5/CSS3/JavaScript with geospatial ESRI ArcGIS API for JavaScript available as the RESTful resources. Django web framework based on the “Model – View – Controller” architectural model represents server-side implementation, where Python is the main programming language used for the application’s business logic. The complete Web-based GIS represents a web portal with a set of services providing a rich instrumentation for the appropriate geophysical Data analysis, processing, and Visualization. Each tool upon execution provides an interactive geospatial image, which is generated according to the user request parameters or by default date-time settings. The proposed web services are freely available at https: //aurora-forecast. ru and https: //geomagnetic. ru through the web browsers.

Managers are confronted with increasing information overload and growing pressure for effective and efficient decision making. The visualisation of Data represents a way to overcome this dilemma and to improve management decision quality. The purpose of this paper is to transfer insights from visualisation research to the managerial accounting context and clarify the impact of visualisation on management accounting reports and decision making. In order to answer the research question, three hypotheses are tackled. Study sample includes experienced managers and accounting students. The empirical results show that supplementary graphs improve decision quality, especially within the manager sample but do not affect decision confidence in a performance evaluation task. Based on results, managers perform poorly when only provided with tables, and they achieve the overall best score when provided with both tables and graphs. Furthermore, the findings showed that managers were more confident in their decisions than students, irrespective of the information presentation.

Background: Over the past two decades, myoelectric signals have been extensively used in rehabilitation technology and hybrid human-machine interfaces. A key challenge in creating self-engineered, cost-effective devices lies in acquiring reliable and accurate myoelectric signals. Additionally, identifying optimal anatomical sites for signal detection remains complex and is addressed in this study. Method: This applied research aims to tackle the outlined challenges through technological development and experimental testing. A Multi-Threading-based Queuing (MTQ) approach is proposed for real-time display and recording of muscle activity within a low-cost, multi-channel surface electromyography (sEMG) system. The technique was tested using raw (R) and feature (F) Datasets via specialized classifiers to categorize sEMG signals from the silent utterance of English vowels captured from three facial muscles of a single healthy volunteer. Results: The proposed low-cost sEMG Data acquisition technique, utilizing MTQ, achieved a mean classification accuracy of 0.91 for both R and F Datasets, surpassing previous techniques for English vowel classification. Model 4, paired with low-cost hardware, attained a remarkable mean accuracy of 0.94, showing improvements between 14.6% and 74.07% over prior studies. Conclusion: The MTQ technique significantly enhances performance compared to existing configurations, suggesting that cost-effective sEMG Data acquisition systems could replace commercial hardware in rehabilitation and human-machine interface applications.

The world is full of Data which is increasing by leaps and bounds. In health care, big Data is becoming common with increased electronic health Data accumulation and/or accessibility to public Data previously held under lock and key. At the same time, health Data Visualization applications have become popular over recent years. Against this background, a review was done to summarize the application of Data Visualization in public health & the challenges faced. Peer‑, reviewed original research articles and review articles searched in Google Scholar and Pubmed Databases that were indexed in the last ten years period, using the keywords “, Big Data”,or “, Data Visualization”,or “, Interactive Visualization techniques. ”,Other related information in books, blogs, and published documents were searched in Google search engine using the same keywords. Contents from the downloaded documents were presented and discussed under three headings viz. (a) the Visualizations that are still current and how they have evolved further, (b) tools or methods that can be used by end‑, users to make their own modifications, (c) the platforms to disseminate them. Usage of different plots in public health is explained with suitable examples using the Data from public health Datasets. From the discussion it can be understood that when big Data is visualized well, it can identify implementation gaps and disparities and accelerate implementation strategies to reach the population groups in most need for interventions. As health administrator may come from diverse specialties, robust training and career development for big Data in public health is the need of the hour.